Implications of impact experiments on honeycomb shielded exterior beam-column joint

Rajeev, Anupoju; Mohotti, Damith; Shelke, Amit

doi:10.1016/j.engstruct.2020.110470

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…Usually, for diagnostics are used: Visual diagnostics, Ultrasonic testing, Thermography; X-Ray testing, Eddy current; Electromagnetic Testing, Acoustic emission measurements, measurements and analysis of vibrations. 8,[13][14][15][16][17] Comparing all mentioned methods can be stated that vibration analysis is one of the most universal and commonly used methods of technical diagnostics since it is suitable for diagnostics of…”

Section: Introductionmentioning

confidence: 99%

“…[3][4][5][6][7][8][9] Nevertheless, limited studies have been carried out on the beam-column subassemblies examined for several deficiencies, such as beams and columns being weak in shear, beams being weak in flexure. [10][11][12][13][14][15][16] Therefore, it is to be remarked that the external beamcolumn joint is the most critical link in the structure that necessitates a thorough study for shock loads. In the present study, an attempt has been made to identify the extent of damage in beam-column subassembly when imparted by the shock loading through data driven health monitoring technique.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC Beam-Column Joint Subjected to Shock Loading

Rajeev

Pamwani

Ojha

et al. 2022

Structural Health Monitoring

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In the present work, a novel technique based on the combination of singular spectral analysis (SSA) and recursive estimate of coefficients of adaptive autoregressive (AR) modelling is employed to identify the damage in the reinforced (RC) beam-column joints. The damage is induced by imparting shock load at the tip of the beam-column joints. The damage is identified with the help of the acceleration response of the healthy and damaged specimens excited by high intensity white noise. The proposed approach has two major components, first, filtering and removing the noise from the dynamic response using the singular spectral analysis and second, modelling the filtered response using adaptive AR process to get the recursive estimate of coefficient matrix for baseline and damage states. The coefficients evaluated for each time instant are presented in a multi-dimensional subspace to form distinct clusters corresponding to a healthy and damaged state. In order to identify and quantify the damage, the geometrical and statistical measures are evaluated that quantifies the segregation of clusters. In total, three distinct measures are used to quantify the damage, namely, Euclidean distance (ED), Mahalanobis distance (MD) and Bhattacharyya distance (BD). The BD accounts the variation in the distribution of both the clusters, thereby shows superior results comparatively than ED and MD. The results of DSFs also manifest the superiority of BD over the other two DSFs. These geometrical and statistical distances are the damage sensitive feature (DSF) to identify and quantify the damage in the specimen due to the shock load. The obtained results of all the DSFs show good consistency with the maximum deformation of the specimen due to shock loading highlighting the accuracy of the proposed algorithm.

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